CQ-VQA: Visual Question Answering on Categorized Questions
This is an incremental improvement for VQA tasks, potentially enhancing accuracy by narrowing answer predictions based on question types.
The paper tackles visual question answering by proposing CQ-VQA, a 2-level hierarchical model that categorizes questions to reduce answer search space and uses distinct classifiers per category, achieving competitive or better performance on the TDIUC dataset.
This paper proposes CQ-VQA, a novel 2-level hierarchical but end-to-end model to solve the task of visual question answering (VQA). The first level of CQ-VQA, referred to as question categorizer (QC), classifies questions to reduce the potential answer search space. The QC uses attended and fused features of the input question and image. The second level, referred to as answer predictor (AP), comprises of a set of distinct classifiers corresponding to each question category. Depending on the question category predicted by QC, only one of the classifiers of AP remains active. The loss functions of QC and AP are aggregated together to make it an end-to-end model. The proposed model (CQ-VQA) is evaluated on the TDIUC dataset and is benchmarked against state-of-the-art approaches. Results indicate competitive or better performance of CQ-VQA.